We are testing a new system for linking grants to scientists.
The funding information displayed below comes from the
NIH Research Portfolio Online Reporting Tools and the
NSF Award Database.
The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
You can help! If you notice any innacuracies, please
sign in and mark grants as correct or incorrect matches.
Sign in to see low-probability grants and correct any errors in linkage between grants and researchers.
High-probability grants
According to our matching algorithm, Sarah Marzen is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2022 — 2023 |
Marzen, Sarah |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Conference: Sensory Prediction, Engineered and Evolved
This workshop will bring together sensory prediction experts to collaborate on an interdisciplinary approach to understanding both the brain and how to improve prediction algorithms. Both fields are well-studied, but the intersection between the two fields is nascent. Though diverse in approach, all participants’ research converges in using a single definition of what one needs to infer in order to predict. This definition has inspired biological work and many prediction algorithms, which work together in tandem: an understanding of the biology has inspired improvements to prediction algorithms, and improvements in prediction algorithms have led to new testable hypotheses about parts of biological organisms. In convening this globally diverse, interdisciplinary session, this workshop advances the scientific community’s ability to solve the prediction problem—how to predict the future given the past. <br/><br/>Scientists predict future input from past input. This can take the form of prediction of natural video, natural audio, or text, which has famously led to such products as large language models and proprietary algorithms for stock market prediction. Organisms and parts of organisms may have evolved to efficiently predict their input as well, and the hypothesis that they do is a cornerstone of theoretical neuroscience. How to design adaptive systems to predict input is still a matter of debate, especially when one has continuous input and a possible prediction state at every point in time. This workshop brings together researchers who approach designing adaptive systems to predict input through the lens of biology with machine learning, information theory, and dynamical systems for a five-day conference in summer of 2023 at the Santa Fe Institute, a mecca for complex systems research that should lead to a unifying framework for sensory prediction.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|
0.922 |